Jason O'Kane: Research Overview

The broad goal of our research is to design algorithms that enable robots and
robot teams to operate autonomously, robustly, and inexpensively in
unstructured and inhospitable environments. Because sensing and uncertainty
are central issues in robotics, it is essential to understand how to solve
robotics problems when sensing is limited and uncertainty abounds. Our work
spans sensor-based algorithmic robotics and related areas, including planning
under uncertainty, artificial intelligence, computational geometry, sensor
networks, and motion planning.

Planning with Severe Sensing Limitations

We have worked on a variety of planning problems for robots with very few
sensors, very unreliable movements, or both. Results that use good
algorithms to overcome such hardware limitations are useful both for reducing
the cost and complexity of the robots we build, and for understanding the
problems themselves with an eye toward solving them with more powerful robots.

Building on our priorwork on robot localization with limited sensing,
one recent series of results deals with navigation problems for
nearly-sensorless mobile robots with bounded but substantial motion errors.
The key observation is that certain kinds of environment features are useful
for reducing uncertainty without explicit sensing. For example, we have shown
that under the right conditions, a robot can localize itself in a corner of its
environment, using only a noisy compass and a contact sensor. We have
developed planning algorithms that exploit these kinds of strategies to enable
long-range indoor navigation of very simple mobile robots.

Pursuit and Tracking

We are developing a collection of algorithms for systematically locating
and following moving targets.

One
branch of this work deals with pursuit and evasion problems, in which a
mobile robot pursuer moves to locate one or more unpredictably moving
evaders. Our results show how to minimize the amount of time needed to
locate the evaders in the worst case, and make significant improvements to the
expected capture time when a probabilistic model for the evaders' movements is
known.

We have also developed algorithms for target tracking by mobile robots
with varying levels sensing ability, including scenarios in which the robot
cooperates with an ambient sensor network in an energy-efficient way,
and in which many robots work together to track a group of moving targets.

Approximated Information States

Much of our work is based on the idea of planning over a space of
information states, which explicitly represent the robot's incomplete
knowledge. However, for platforms in which there are strong limits on
computation power (for example, due to limitations in space, weight, power, or
cost), it may not be practical to compute those information states directly.
As an alternative, we have proposed methods for maintaining low-complexity
geometric representations of uncertainty that retain provable relationships
(such as, for example, set containment) to the underlying "true" information
state. Our results show that, in many cases, surprisingly simple
representations are sufficient for the robot to make good decisions.

CHARLIE and Low-Cost Assistive Robots

Because they can perform some mechanical tasks predictably and consistently,
robots are well suited as part of an early intervention strategy for
some autistic children, especially those who tend to perceive them as
nonthreatening and intrinsically interesting. Research has demonstrated that
robot-assisted autism therapy promotes increased speech and increased
child-initiated interactions in children with Autism Spectrum Disorder (ASD).
To make such robots more broadly accessible, we have designed and built a
low-cost, interactive robot named CHARLIE (CHild-centered Adaptive Robot
for Learning in an Interactive Environment). We have developed software
for CHARLIE that autonomously detects and adapts to the perceived interest
level of its user.
To enhance the ability of robots like CHARLIE to respond to changes in their
user's affective states, we are also developing new techniques for capturing
breathing and heart rates remotely using a high precision, single-point
infrared temperature sensor.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.